{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "056498db-23b5-471b-8ee6-ccc1bbf8560f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "imdb=tf.keras.datasets.imdb\n",
    "(x_train,y_train),(x_test,y_test)=imdb.load_data(num_words=4000)\n",
    "x_train=tf.keras.preprocessing.sequence.pad_sequences(x_train,padding='post',maxlen=400,truncating='post')\n",
    "x_test=tf.keras.preprocessing.sequence.pad_sequences(x_test,padding='post',maxlen=400,truncating='post')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "627d755b-d59e-4f3b-806a-f6fadcacdba4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\embedding.py:90: UserWarning: Argument `input_length` is deprecated. Just remove it.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Embedding</span>)                │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                    │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ gru (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GRU</span>)                            │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ ?                           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ ?                           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ embedding (\u001b[38;5;33mEmbedding\u001b[0m)                │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)                    │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ gru (\u001b[38;5;33mGRU\u001b[0m)                            │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)                  │ ?                           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ ?                           │     \u001b[38;5;34m0\u001b[0m (unbuilt) │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=tf.keras.models.Sequential()\n",
    "model.add(tf.keras.layers.Embedding(output_dim=32,input_dim=4000,input_length=400))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.GRU(64))\n",
    "model.add(tf.keras.layers.Dropout(0.3))\n",
    "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a4f00e4e-447c-4afb-b575-9d934fc3e8d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m41s\u001b[0m 123ms/step - accuracy: 0.5033 - loss: 0.6934 - val_accuracy: 0.4938 - val_loss: 0.6945\n",
      "Epoch 2/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m40s\u001b[0m 128ms/step - accuracy: 0.5026 - loss: 0.6929 - val_accuracy: 0.4938 - val_loss: 0.6944\n",
      "Epoch 3/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 124ms/step - accuracy: 0.5039 - loss: 0.6923 - val_accuracy: 0.5018 - val_loss: 0.6942\n",
      "Epoch 4/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 124ms/step - accuracy: 0.5161 - loss: 0.6914 - val_accuracy: 0.5032 - val_loss: 0.6942\n",
      "Epoch 5/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 124ms/step - accuracy: 0.5207 - loss: 0.6906 - val_accuracy: 0.4996 - val_loss: 0.6945\n",
      "Epoch 6/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 125ms/step - accuracy: 0.5208 - loss: 0.6891 - val_accuracy: 0.5022 - val_loss: 0.6961\n",
      "Epoch 7/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 124ms/step - accuracy: 0.5180 - loss: 0.6879 - val_accuracy: 0.5042 - val_loss: 0.6950\n",
      "Epoch 8/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 123ms/step - accuracy: 0.5262 - loss: 0.6846 - val_accuracy: 0.5098 - val_loss: 0.6963\n",
      "Epoch 9/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 124ms/step - accuracy: 0.5312 - loss: 0.6810 - val_accuracy: 0.5112 - val_loss: 0.6980\n",
      "Epoch 10/10\n",
      "\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m39s\u001b[0m 125ms/step - accuracy: 0.5284 - loss: 0.6800 - val_accuracy: 0.5090 - val_loss: 0.6980\n",
      "391/391 - 13s - 34ms/step - accuracy: 0.5077 - loss: 0.6972\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.6971874833106995, 0.5077199935913086]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='rmsprop',loss='binary_crossentropy',metrics=['accuracy'])\n",
    "history=model.fit(x_train,y_train,batch_size=64,epochs=10,validation_split=0.2)\n",
    "model.evaluate(x_test,y_test,batch_size=64,verbose=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "8ad690b2-3dd4-4cc1-96d2-a51fa52a57c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x300 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss=history.history['loss']\n",
    "acc=history.history['accuracy']\n",
    "val_loss=history.history['val_loss']\n",
    "val_acc=history.history['val_accuracy']\n",
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(121)\n",
    "plt.plot(loss,color='b',label='train')\n",
    "plt.plot(val_loss,color='r',label='validata')\n",
    "plt.ylabel('loss')\n",
    "plt.legend()\n",
    "plt.subplot(122)\n",
    "plt.plot(acc,color='b',label='train')\n",
    "plt.plot(val_acc,color='r',label='validate')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cb2dce33-b563-4a9e-b1fa-b673095c500a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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